To search for the optimal machine learning model, we based our solution on [1]}, by performing an automatic random search [2]} over a set of several machine learning algorithms and their inner parameters. So, in this work, to search the optimal model and its inner parameters, we performed a random search over the space that includes the following models: a random forest, an extremely-randomized forest, a random grid of generalized linear models, a random grid of XGboost, a random grid of gradient boosting machines (GBM), a random grid of deep neural networks. After searching these models, 2 stacked ensembles were created, the first one comprised of all models evaluated, and the other one, containing the best model of each type. In the end, the model with the best performance on the validation set is the one selected to be in the architecture of the proposed method.
